Compression fractures detection on CT

Amir Bar; Lior Wolf; Orna Bergman Amitai; Eyal Toledano; Eldad Elnekave

doi:10.1117/12.2249635

3 March 2017 Compression fractures detection on CT

Amir Bar, Lior Wolf, Orna Bergman Amitai, Eyal Toledano, Eldad Elnekave

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013440 (2017) https://doi.org/10.1117/12.2249635
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.

Citation Download Citation

Amir Bar, Lior Wolf, Orna Bergman Amitai, Eyal Toledano, and Eldad Elnekave "Compression fractures detection on CT", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013440 (3 March 2017); https://doi.org/10.1117/12.2249635

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